from __future__ import annotations

import json
import typing
import warnings
from typing import Any, Literal

import numpy as np
import pandas as pd
from pandas import DataFrame, Series

import shapely.errors
from shapely.geometry import mapping, shape
from shapely.geometry.base import BaseGeometry

import geopandas
from geopandas.array import GeometryArray, GeometryDtype, from_shapely, to_wkb, to_wkt
from geopandas.base import GeoPandasBase, is_geometry_type
from geopandas.explore import _explore
from geopandas.geoseries import GeoSeries

from ._compat import HAS_PYPROJ, PANDAS_GE_30
from ._decorator import doc

if PANDAS_GE_30:
    from pandas.core.accessor import Accessor
else:
    from pandas.core.accessor import CachedAccessor as Accessor


if typing.TYPE_CHECKING:
    import os
    from collections.abc import Iterable

    import folium
    import sqlalchemy.text

    from pyproj import CRS

    from geopandas.io.arrow import (
        PARQUET_GEOMETRY_ENCODINGS,
        SUPPORTED_VERSIONS_LITERAL,
    )


def _ensure_geometry(data, crs: Any | None = None) -> GeoSeries | GeometryArray:
    """
    Ensure the data is of geometry dtype or converted to it.

    If input is a (Geo)Series, output is a GeoSeries, otherwise output
    is GeometryArray.

    If the input is a GeometryDtype with a set CRS, `crs` is ignored.
    """
    if is_geometry_type(data):
        if isinstance(data, Series):
            data = GeoSeries(data)
        if data.crs is None and crs is not None:
            # Avoids caching issues/crs sharing issues
            data = data.copy()
            if isinstance(data, GeometryArray):
                data.crs = crs
            else:
                data.array.crs = crs
        return data
    else:
        if isinstance(data, Series):
            out = from_shapely(np.asarray(data), crs=crs)
            return GeoSeries(out, index=data.index, name=data.name)
        else:
            out = from_shapely(data, crs=crs)
            return out


crs_mismatch_error = (
    "CRS mismatch between CRS of the passed geometries "
    "and 'crs'. Use 'GeoDataFrame.set_crs(crs, "
    "allow_override=True)' to overwrite CRS or "
    "'GeoDataFrame.to_crs(crs)' to reproject geometries. "
)


class GeoDataFrame(GeoPandasBase, DataFrame):
    """A GeoDataFrame object is a pandas.DataFrame that has one or more columns
    containing geometry.

    In addition to the standard DataFrame constructor arguments,
    GeoDataFrame also accepts the following keyword arguments:

    Parameters
    ----------
    crs : value (optional)
        Coordinate Reference System of the geometry objects. Can be anything accepted by
        :meth:`pyproj.CRS.from_user_input() <pyproj.crs.CRS.from_user_input>`,
        such as an authority string (eg "EPSG:4326") or a WKT string.
    geometry : str or array-like (optional)
        Value to use as the active geometry column.
        If str, treated as column name to use. If array-like, it will be
        added as new column named 'geometry' on the GeoDataFrame and set as the
        active geometry column.

        Note that if ``geometry`` is a (Geo)Series with a
        name, the name will not be used, a column named "geometry" will still be
        added. To preserve the name, you can use :meth:`~GeoDataFrame.rename_geometry`
        to update the geometry column name.

    Examples
    --------
    Constructing GeoDataFrame from a dictionary.

    >>> from shapely.geometry import Point
    >>> d = {'col1': ['name1', 'name2'], 'geometry': [Point(1, 2), Point(2, 1)]}
    >>> gdf = geopandas.GeoDataFrame(d, crs="EPSG:4326")
    >>> gdf
        col1     geometry
    0  name1  POINT (1 2)
    1  name2  POINT (2 1)

    Notice that the inferred dtype of 'geometry' columns is geometry.

    >>> gdf.dtypes
    col1          object
    geometry    geometry
    dtype: object

    Constructing GeoDataFrame from a pandas DataFrame with a column of WKT geometries:

    >>> import pandas as pd
    >>> d = {'col1': ['name1', 'name2'], 'wkt': ['POINT (1 2)', 'POINT (2 1)']}
    >>> df = pd.DataFrame(d)
    >>> gs = geopandas.GeoSeries.from_wkt(df['wkt'])
    >>> gdf = geopandas.GeoDataFrame(df, geometry=gs, crs="EPSG:4326")
    >>> gdf
        col1          wkt     geometry
    0  name1  POINT (1 2)  POINT (1 2)
    1  name2  POINT (2 1)  POINT (2 1)

    See Also
    --------
    GeoSeries : Series object designed to store shapely geometry objects
    """

    _metadata = ["_geometry_column_name"]

    _internal_names = DataFrame._internal_names + ["geometry"]
    _internal_names_set = set(_internal_names)

    _geometry_column_name = None

    def __init__(
        self,
        data=None,
        *args,
        geometry: Any | None = None,
        crs: Any | None = None,
        **kwargs,
    ):
        if (
            kwargs.get("copy") is None
            and isinstance(data, DataFrame)
            and not isinstance(data, GeoDataFrame)
        ):
            kwargs.update(copy=True)

        if data is None and "columns" not in kwargs:
            # pandas will interpret "str" as object dtype for pandas < 3 and
            # as string dtype for pandas >= 3. This ensures we still get string
            # columns when doing GeoDataFrame(geometry=[..])
            kwargs["columns"] = pd.Index([], dtype="str")

        super().__init__(data, *args, **kwargs)

        if isinstance(data, DataFrame) and data.attrs:
            self.attrs = data.attrs

        # set_geometry ensures the geometry data have the proper dtype,
        # but is not called if `geometry=None` ('geometry' column present
        # in the data), so therefore need to ensure it here manually
        # but within a try/except because currently non-geometries are
        # allowed in that case
        # TODO do we want to raise / return normal DataFrame in this case?

        # if gdf passed in and geo_col is set, we use that for geometry
        if geometry is None and isinstance(data, GeoDataFrame):
            self._geometry_column_name = data._geometry_column_name
            if crs is not None and data.crs != crs:
                raise ValueError(crs_mismatch_error)

        if (
            geometry is None
            and self.columns.nlevels == 1
            and "geometry" in self.columns
        ):
            # Check for multiple columns with name "geometry". If there are,
            # self["geometry"] is a gdf and constructor gets recursively recalled
            # by pandas internals trying to access this
            if (self.columns == "geometry").sum() > 1:
                raise ValueError(
                    "GeoDataFrame does not support multiple columns "
                    "using the geometry column name 'geometry'."
                )

            # only if we have actual geometry values -> call set_geometry
            if (
                hasattr(self["geometry"].values, "crs")
                and self["geometry"].values.crs
                and crs
                and not self["geometry"].values.crs == crs
            ):
                raise ValueError(crs_mismatch_error)
            # If "geometry" is potentially coercible to geometry, we try and convert it
            geom_dtype = self["geometry"].dtype
            if (
                geom_dtype == "geometry"  # noqa: PLR1714
                or geom_dtype == "object"
                # special case for geometry = [], has float dtype
                or (len(self) == 0 and geom_dtype == "float")
                # special case for geometry = [np.nan]
                or ((not self.empty) and self["geometry"].isna().all())
            ):
                try:
                    self["geometry"] = _ensure_geometry(self["geometry"].values, crs)
                except TypeError:
                    pass
                else:
                    # feed through to call set geometry below
                    geometry = "geometry"

        if geometry is not None:
            if (
                hasattr(geometry, "crs")
                and geometry.crs
                and crs
                and not geometry.crs == crs
            ):
                raise ValueError(crs_mismatch_error)

            if isinstance(geometry, pd.Series) and geometry.name not in (
                "geometry",
                None,
            ):
                # __init__ always creates geometry col named "geometry"
                # rename as `set_geometry` respects the given series name
                geometry = geometry.rename("geometry")

            self.set_geometry(geometry, inplace=True, crs=crs)

        if geometry is None and crs:
            raise ValueError(
                "Assigning CRS to a GeoDataFrame without a geometry column is not "
                "supported. Supply geometry using the 'geometry=' keyword argument, "
                "or by providing a DataFrame with column name 'geometry'",
            )

    def __setattr__(self, attr, val):
        # have to special case geometry b/c pandas tries to use as column...
        if attr == "geometry":
            object.__setattr__(self, attr, val)
        else:
            super().__setattr__(attr, val)

    def _get_geometry(self) -> GeoSeries:
        if self._geometry_column_name not in self:
            if self._geometry_column_name is None:
                msg = (
                    "You are calling a geospatial method on the GeoDataFrame, "
                    "but the active geometry column to use has not been set. "
                )
            else:
                msg = (
                    "You are calling a geospatial method on the GeoDataFrame, "
                    f"but the active geometry column ('{self._geometry_column_name}') "
                    "is not present. "
                )
            geo_cols = list(self.columns[self.dtypes == "geometry"])
            if len(geo_cols) > 0:
                msg += (
                    f"\nThere are columns with geometry data type ({geo_cols}), and "
                    "you can either set one as the active geometry with "
                    'df.set_geometry("name") or access the column as a '
                    'GeoSeries (df["name"]) and call the method directly on it.'
                )
            else:
                msg += (
                    "\nThere are no existing columns with geometry data type. You can "
                    "add a geometry column as the active geometry column with "
                    "df.set_geometry. "
                )

            raise AttributeError(msg)
        return self[self._geometry_column_name]

    def _set_geometry(self, col):
        if not pd.api.types.is_list_like(col):
            raise ValueError("Must use a list-like to set the geometry property")
        self._persist_old_default_geometry_colname()
        self.set_geometry(col, inplace=True)

    geometry = property(
        fget=_get_geometry, fset=_set_geometry, doc="Geometry data for GeoDataFrame"
    )

    @typing.overload
    def set_geometry(
        self,
        col,
        drop: bool | None = ...,
        inplace: Literal[True] = ...,
        crs: Any | None = ...,
    ) -> None: ...

    @typing.overload
    def set_geometry(
        self,
        col,
        drop: bool | None = ...,
        inplace: Literal[False] = ...,
        crs: Any | None = ...,
    ) -> GeoDataFrame: ...

    def set_geometry(
        self,
        col,
        drop: bool | None = None,
        inplace: bool = False,
        crs: Any | None = None,
    ) -> GeoDataFrame | None:
        """
        Set the GeoDataFrame geometry using either an existing column or
        the specified input. By default yields a new object.

        The original geometry column is replaced with the input.

        Parameters
        ----------
        col : column label or array-like
            An existing column name or values to set as the new geometry column.
            If values (array-like, (Geo)Series) are passed, then if they are named
            (Series) the new geometry column will have the corresponding name,
            otherwise the existing geometry column will be replaced. If there is
            no existing geometry column, the new geometry column will use the
            default name "geometry".
        drop : boolean, default False
            When specifying a named Series or an existing column name for `col`,
            controls if the previous geometry column should be dropped from the
            result. The default of False keeps both the old and new geometry column.

            .. deprecated:: 1.0.0

        inplace : boolean, default False
            Modify the GeoDataFrame in place (do not create a new object)
        crs : pyproj.CRS, optional
            Coordinate system to use. The value can be anything accepted
            by :meth:`pyproj.CRS.from_user_input() <pyproj.crs.CRS.from_user_input>`,
            such as an authority string (eg "EPSG:4326") or a WKT string.
            If passed, overrides both DataFrame and col's crs.
            Otherwise, tries to get crs from passed col values or DataFrame.

        Examples
        --------
        >>> from shapely.geometry import Point
        >>> d = {'col1': ['name1', 'name2'], 'geometry': [Point(1, 2), Point(2, 1)]}
        >>> gdf = geopandas.GeoDataFrame(d, crs="EPSG:4326")
        >>> gdf
            col1     geometry
        0  name1  POINT (1 2)
        1  name2  POINT (2 1)

        Passing an array:

        >>> df1 = gdf.set_geometry([Point(0,0), Point(1,1)])
        >>> df1
            col1     geometry
        0  name1  POINT (0 0)
        1  name2  POINT (1 1)

        Using existing column:

        >>> gdf["buffered"] = gdf.buffer(2)
        >>> df2 = gdf.set_geometry("buffered")
        >>> df2.geometry
        0    POLYGON ((3 2, 2.99037 1.80397, 2.96157 1.6098...
        1    POLYGON ((4 1, 3.99037 0.80397, 3.96157 0.6098...
        Name: buffered, dtype: geometry

        Returns
        -------
        GeoDataFrame

        See Also
        --------
        GeoDataFrame.rename_geometry : rename an active geometry column
        """
        # Most of the code here is taken from DataFrame.set_index()
        if inplace:
            frame = self
        else:
            if PANDAS_GE_30:
                frame = self.copy(deep=False)
            else:
                frame = self.copy()

        geo_column_name = self._geometry_column_name

        if geo_column_name is None:
            geo_column_name = "geometry"
        if isinstance(col, Series | list | np.ndarray | GeometryArray):
            if drop:
                msg = (
                    "The `drop` keyword argument is deprecated and has no effect when "
                    "`col` is an array-like value. You should stop passing `drop` to "
                    "`set_geometry` when this is the case."
                )
                warnings.warn(msg, category=FutureWarning, stacklevel=2)
            if isinstance(col, Series) and col.name is not None:
                geo_column_name = col.name

            level = col
        elif hasattr(col, "ndim") and col.ndim > 1:
            raise ValueError("Must pass array with one dimension only.")
        else:  # should be a colname
            try:
                level = frame[col]
            except KeyError:
                raise ValueError(f"Unknown column {col}")
            if isinstance(level, DataFrame):
                raise ValueError(
                    "GeoDataFrame does not support setting the geometry column where "
                    "the column name is shared by multiple columns."
                )

            given_colname_drop_msg = (
                "The `drop` keyword argument is deprecated and in future the only "
                "supported behaviour will match drop=False. To silence this "
                "warning and adopt the future behaviour, stop providing "
                "`drop` as a keyword to `set_geometry`. To replicate the "
                "`drop=True` behaviour you should update "
                "your code to\n`geo_col_name = gdf.active_geometry_name;"
                " gdf.set_geometry(new_geo_col).drop("
                "columns=geo_col_name).rename_geometry(geo_col_name)`."
            )

            if drop is False:  # specifically False, not falsy i.e. None
                # User supplied False explicitly, but arg is deprecated
                warnings.warn(
                    given_colname_drop_msg,
                    category=FutureWarning,
                    stacklevel=2,
                )
            if drop:
                del frame[col]
                frame.__class__ = GeoDataFrame
                # revert the casting done in __delitem__, keep gdf
                warnings.warn(
                    given_colname_drop_msg,
                    category=FutureWarning,
                    stacklevel=2,
                )
            else:
                # if not dropping, set the active geometry name to the given col name
                geo_column_name = col

        if not crs:
            crs = getattr(level, "crs", None)

        # Check that we are using a listlike of geometries
        level = _ensure_geometry(level, crs=crs)
        # ensure_geometry only sets crs on level if it has crs==None
        if isinstance(level, GeoSeries):
            level.array.crs = crs
        else:
            level.crs = crs
        # update _geometry_column_name prior to assignment
        # to avoid default is None warning
        frame._geometry_column_name = geo_column_name
        frame[geo_column_name] = level

        if not inplace:
            return frame

    @typing.overload
    def rename_geometry(
        self,
        col: str,
        inplace: Literal[True] = ...,
    ) -> None: ...

    @typing.overload
    def rename_geometry(
        self,
        col: str,
        inplace: Literal[False] = ...,
    ) -> GeoDataFrame: ...

    def rename_geometry(self, col: str, inplace: bool = False) -> GeoDataFrame | None:
        """Rename the GeoDataFrame geometry column to the specified name.

        By default yields a new object.

        The original geometry column is replaced with the input.

        Parameters
        ----------
        col : new geometry column label
        inplace : boolean, default False
            Modify the GeoDataFrame in place (do not create a new object)

        Examples
        --------
        >>> from shapely.geometry import Point
        >>> d = {'col1': ['name1', 'name2'], 'geometry': [Point(1, 2), Point(2, 1)]}
        >>> df = geopandas.GeoDataFrame(d, crs="EPSG:4326")
        >>> df1 = df.rename_geometry('geom1')
        >>> df1.geometry.name
        'geom1'
        >>> df.rename_geometry('geom1', inplace=True)
        >>> df.geometry.name
        'geom1'


        See Also
        --------
        GeoDataFrame.set_geometry : set the active geometry
        """
        geometry_col = self.geometry.name
        if col in self.columns:
            raise ValueError(f"Column named {col} already exists")
        else:
            if not inplace:
                return self.rename(columns={geometry_col: col}).set_geometry(
                    col, inplace=inplace
                )
            self.rename(columns={geometry_col: col}, inplace=inplace)
            self.set_geometry(col, inplace=inplace)

    @property
    def active_geometry_name(self) -> Any:
        """Return the name of the active geometry column.

        Returns a name if a GeoDataFrame has an active geometry column set,
        otherwise returns None. The return type is usually a string, but may be
        an integer, tuple or other hashable, depending on the contents of the
        dataframe columns.

        You can also access the active geometry column using the
        ``.geometry`` property. You can set a GeoSeries to be an active geometry
        using the :meth:`~GeoDataFrame.set_geometry` method.

        Returns
        -------
        str or other index label supported by pandas
            name of an active geometry column or None

        See Also
        --------
        GeoDataFrame.set_geometry : set the active geometry
        """
        return self._geometry_column_name

    @property
    def crs(self) -> CRS:
        """
        The Coordinate Reference System (CRS) represented as a ``pyproj.CRS``
        object.

        Returns
        -------
        ``pyproj.CRS`` | None
            CRS assigned to an active geometry column

        Examples
        --------
        >>> gdf.crs  # doctest: +SKIP
        <Geographic 2D CRS: EPSG:4326>
        Name: WGS 84
        Axis Info [ellipsoidal]:
        - Lat[north]: Geodetic latitude (degree)
        - Lon[east]: Geodetic longitude (degree)
        Area of Use:
        - name: World
        - bounds: (-180.0, -90.0, 180.0, 90.0)
        Datum: World Geodetic System 1984
        - Ellipsoid: WGS 84
        - Prime Meridian: Greenwich

        See Also
        --------
        GeoDataFrame.set_crs : assign CRS
        GeoDataFrame.to_crs : re-project to another CRS

        """
        try:
            return self.geometry.crs
        except AttributeError:
            raise AttributeError(
                "The CRS attribute of a GeoDataFrame without an active "
                "geometry column is not defined. Use GeoDataFrame.set_geometry "
                "to set the active geometry column."
            )

    @crs.setter
    def crs(self, value) -> None:
        """Set the value of the crs."""
        if self._geometry_column_name is None:
            raise ValueError(
                "Assigning CRS to a GeoDataFrame without a geometry column is not "
                "supported. Use GeoDataFrame.set_geometry to set the active "
                "geometry column.",
            )

        if hasattr(self.geometry.values, "crs"):
            if self.crs is not None:
                warnings.warn(
                    "Overriding the CRS of a GeoDataFrame that already has CRS. "
                    "This unsafe behavior will be deprecated in future versions. "
                    "Use GeoDataFrame.set_crs method instead",
                    stacklevel=2,
                    category=FutureWarning,
                )
            self.geometry.values.crs = value
        else:
            # column called 'geometry' without geometry
            raise ValueError(
                "Assigning CRS to a GeoDataFrame without an active geometry "
                "column is not supported. Use GeoDataFrame.set_geometry to set "
                "the active geometry column.",
            )

    def __setstate__(self, state) -> None:
        # overriding DataFrame method for compat with older pickles (CRS handling)
        crs = None
        if isinstance(state, dict):
            if "crs" in state and "_crs" not in state:
                crs = state.pop("crs", None)
            else:
                crs = state.pop("_crs", None)
            if crs is not None and not HAS_PYPROJ:
                raise ImportError(
                    "Unpickling a GeoDataFrame with CRS requires the 'pyproj' package, "
                    "but it is not installed or does not import correctly. "
                )
            elif crs is not None:
                from pyproj import CRS

                crs = CRS.from_user_input(crs)

        super().__setstate__(state)

        # for some versions that didn't yet have CRS at array level -> crs is set
        # at GeoDataFrame level with '_crs' (and not 'crs'), so without propagating
        # to the GeoSeries/GeometryArray
        try:
            if crs is not None:
                if self.geometry.values.crs is None:
                    self.crs = crs
        except Exception:
            pass

    @classmethod
    def from_dict(
        cls,
        data: dict,
        geometry=None,
        crs: Any | None = None,
        **kwargs,
    ) -> GeoDataFrame:
        """Construct GeoDataFrame from dict of array-like or dicts by
        overriding DataFrame.from_dict method with geometry and crs.

        Parameters
        ----------
        data : dict
            Of the form {field : array-like} or {field : dict}.
        geometry : str or array (optional)
            If str, column to use as geometry. If array, will be set as 'geometry'
            column on GeoDataFrame.
        crs : str or dict (optional)
            Coordinate reference system to set on the resulting frame.
        kwargs : key-word arguments
            These arguments are passed to DataFrame.from_dict

        Returns
        -------
        GeoDataFrame

        """
        dataframe = DataFrame.from_dict(data, **kwargs)
        return cls(dataframe, geometry=geometry, crs=crs)

    @classmethod
    def from_file(cls, filename: os.PathLike | typing.IO, **kwargs) -> GeoDataFrame:
        """Alternate constructor to create a ``GeoDataFrame`` from a file.

        It is recommended to use :func:`geopandas.read_file` instead.

        Can load a ``GeoDataFrame`` from a file in any format recognized by
        `pyogrio`. See http://pyogrio.readthedocs.io/ for details.

        Parameters
        ----------
        filename : str
            File path or file handle to read from. Depending on which kwargs
            are included, the content of filename may vary. See
            :func:`pyogrio.read_dataframe` for usage details.
        kwargs : key-word arguments
            These arguments are passed to :func:`pyogrio.read_dataframe`, and can be
            used to access multi-layer data, data stored within archives (zip files),
            etc.

        Examples
        --------
        >>> import geodatasets
        >>> path = geodatasets.get_path('nybb')
        >>> gdf = geopandas.GeoDataFrame.from_file(path)
        >>> gdf  # doctest: +SKIP
           BoroCode       BoroName     Shape_Leng    Shape_Area                 \
                          geometry
        0         5  Staten Island  330470.010332  1.623820e+09  MULTIPOLYGON ((\
(970217.022 145643.332, 970227....
        1         4         Queens  896344.047763  3.045213e+09  MULTIPOLYGON ((\
(1029606.077 156073.814, 102957...
        2         3       Brooklyn  741080.523166  1.937479e+09  MULTIPOLYGON ((\
(1021176.479 151374.797, 102100...
        3         1      Manhattan  359299.096471  6.364715e+08  MULTIPOLYGON ((\
(981219.056 188655.316, 980940....
        4         2          Bronx  464392.991824  1.186925e+09  MULTIPOLYGON ((\
(1012821.806 229228.265, 101278...

        The recommended method of reading files is :func:`geopandas.read_file`:

        >>> gdf = geopandas.read_file(path)

        See Also
        --------
        read_file : read file to GeoDataFrame
        GeoDataFrame.to_file : write GeoDataFrame to file

        """
        return geopandas.io.file._read_file(filename, **kwargs)

    @classmethod
    def from_features(
        cls, features, crs: Any | None = None, columns: Iterable[str] | None = None
    ) -> GeoDataFrame:
        """
        Alternate constructor to create GeoDataFrame from an iterable of
        features or a feature collection.

        Parameters
        ----------
        features
            - Iterable of features, where each element must be a feature
              dictionary or implement the __geo_interface__.
            - Feature collection, where the 'features' key contains an
              iterable of features.
            - Object holding a feature collection that implements the
              ``__geo_interface__``.
        crs : str or dict (optional)
            Coordinate reference system to set on the resulting frame.
        columns : list of column names, optional
            Optionally specify the column names to include in the output frame.
            This does not overwrite the property names of the input, but can
            ensure a consistent output format.

        Returns
        -------
        GeoDataFrame

        Notes
        -----
        For more information about the ``__geo_interface__``, see
        https://gist.github.com/sgillies/2217756

        Examples
        --------
        >>> feature_coll = {
        ...     "type": "FeatureCollection",
        ...     "features": [
        ...         {
        ...             "id": "0",
        ...             "type": "Feature",
        ...             "properties": {"col1": "name1"},
        ...             "geometry": {"type": "Point", "coordinates": (1.0, 2.0)},
        ...             "bbox": (1.0, 2.0, 1.0, 2.0),
        ...         },
        ...         {
        ...             "id": "1",
        ...             "type": "Feature",
        ...             "properties": {"col1": "name2"},
        ...             "geometry": {"type": "Point", "coordinates": (2.0, 1.0)},
        ...             "bbox": (2.0, 1.0, 2.0, 1.0),
        ...         },
        ...     ],
        ...     "bbox": (1.0, 1.0, 2.0, 2.0),
        ... }
        >>> df = geopandas.GeoDataFrame.from_features(feature_coll)
        >>> df
              geometry   col1
        0  POINT (1 2)  name1
        1  POINT (2 1)  name2

        """
        # Handle feature collections
        if hasattr(features, "__geo_interface__"):
            fs = features.__geo_interface__
        else:
            fs = features

        if isinstance(fs, dict) and fs.get("type") == "FeatureCollection":
            features_lst = fs["features"]
        else:
            features_lst = features

        rows = []
        for feature in features_lst:
            # load geometry
            if hasattr(feature, "__geo_interface__"):
                feature = feature.__geo_interface__
            row = {
                "geometry": shape(feature["geometry"]) if feature["geometry"] else None
            }
            # load properties
            properties = feature.get("properties") or {}
            row.update(properties)
            rows.append(row)
        return cls(rows, columns=columns, crs=crs)

    @classmethod
    def from_postgis(
        cls,
        sql: str | sqlalchemy.text,
        con,
        geom_col: str = "geom",
        crs: Any | None = None,
        index_col: str | list[str] | None = None,
        coerce_float: bool = True,
        parse_dates: list | dict | None = None,
        params: list | tuple | dict | None = None,
        chunksize: int | None = None,
    ) -> GeoDataFrame:
        """
        Alternate constructor to create a ``GeoDataFrame`` from a sql query
        containing a geometry column in WKB representation.

        Parameters
        ----------
        sql : string
        con : sqlalchemy.engine.Connection or sqlalchemy.engine.Engine
        geom_col : string, default 'geom'
            column name to convert to shapely geometries
        crs : optional
            Coordinate reference system to use for the returned GeoDataFrame
        index_col : string or list of strings, optional, default: None
            Column(s) to set as index(MultiIndex)
        coerce_float : boolean, default True
            Attempt to convert values of non-string, non-numeric objects (like
            decimal.Decimal) to floating point, useful for SQL result sets
        parse_dates : list or dict, default None
            - List of column names to parse as dates.
            - Dict of ``{column_name: format string}`` where format string is
              strftime compatible in case of parsing string times, or is one of
              (D, s, ns, ms, us) in case of parsing integer timestamps.
            - Dict of ``{column_name: arg dict}``, where the arg dict
              corresponds to the keyword arguments of
              :func:`pandas.to_datetime`. Especially useful with databases
              without native Datetime support, such as SQLite.
        params : list, tuple or dict, optional, default None
            List of parameters to pass to execute method.
        chunksize : int, default None
            If specified, return an iterator where chunksize is the number
            of rows to include in each chunk.

        Examples
        --------
        PostGIS

        >>> from sqlalchemy import create_engine  # doctest: +SKIP
        >>> db_connection_url = "postgresql://myusername:mypassword@myhost:5432/mydb"
        >>> con = create_engine(db_connection_url)  # doctest: +SKIP
        >>> sql = "SELECT geom, highway FROM roads"
        >>> df = geopandas.GeoDataFrame.from_postgis(sql, con)  # doctest: +SKIP

        SpatiaLite

        >>> sql = "SELECT ST_Binary(geom) AS geom, highway FROM roads"
        >>> df = geopandas.GeoDataFrame.from_postgis(sql, con)  # doctest: +SKIP

        The recommended method of reading from PostGIS is
        :func:`geopandas.read_postgis`:

        >>> df = geopandas.read_postgis(sql, con)  # doctest: +SKIP

        See Also
        --------
        geopandas.read_postgis : read PostGIS database to GeoDataFrame
        """
        df = geopandas.io.sql._read_postgis(
            sql,
            con,
            geom_col=geom_col,
            crs=crs,
            index_col=index_col,
            coerce_float=coerce_float,
            parse_dates=parse_dates,
            params=params,
            chunksize=chunksize,
        )

        return df

    @classmethod
    def from_arrow(
        cls, table, geometry: str | None = None, to_pandas_kwargs: dict | None = None
    ):
        """
        Construct a GeoDataFrame from an Arrow table object based on GeoArrow
        extension types.

        See https://geoarrow.org/ for details on the GeoArrow specification.

        This functions accepts any tabular Arrow object implementing
        the `Arrow PyCapsule Protocol`_ (i.e. having an ``__arrow_c_array__``
        or ``__arrow_c_stream__`` method).

        .. _Arrow PyCapsule Protocol: https://arrow.apache.org/docs/format/CDataInterface/PyCapsuleInterface.html

        .. versionadded:: 1.0

        Parameters
        ----------
        table : pyarrow.Table or Arrow-compatible table
            Any tabular object implementing the Arrow PyCapsule Protocol
            (i.e. has an ``__arrow_c_array__`` or ``__arrow_c_stream__``
            method). This table should have at least one column with a
            geoarrow geometry type.
        geometry : str, default None
            The name of the geometry column to set as the active geometry
            column. If None, the first geometry column found will be used.
        to_pandas_kwargs : dict, optional
            Arguments passed to the `pa.Table.to_pandas` method for non-geometry
            columns. This can be used to control the behavior of the conversion of the
            non-geometry columns to a pandas DataFrame. For example, you can use this
            to control the dtype conversion of the columns. By default, the `to_pandas`
            method is called with no additional arguments.

        Returns
        -------
        GeoDataFrame

        """
        from geopandas.io._geoarrow import arrow_to_geopandas

        return arrow_to_geopandas(
            table, geometry=geometry, to_pandas_kwargs=to_pandas_kwargs
        )

    def to_json(
        self,
        na: Literal["null", "drop", "keep"] = "null",
        show_bbox: bool = False,
        drop_id: bool = False,
        to_wgs84: bool = False,
        **kwargs,
    ) -> str:
        """Return a GeoJSON representation of the ``GeoDataFrame`` as a string.

        Parameters
        ----------
        na : {'null', 'drop', 'keep'}, default 'null'
            Indicates how to output missing (NaN) values in the GeoDataFrame.
            See below.
        show_bbox : bool, optional, default: False
            Include bbox (bounds) in the geojson
        drop_id : bool, default: False
            Whether to retain the index of the GeoDataFrame as the id property
            in the generated GeoJSON. Default is False, but may want True
            if the index is just arbitrary row numbers.
        to_wgs84: bool, optional, default: False
            If the CRS is set on the active geometry column it is exported as
            WGS84 (EPSG:4326) to meet the `2016 GeoJSON specification
            <https://tools.ietf.org/html/rfc7946>`_.
            Set to True to force re-projection and set to False to ignore CRS. False by
            default.

        Notes
        -----
        The remaining *kwargs* are passed to json.dumps().

        Missing (NaN) values in the GeoDataFrame can be represented as follows:

        - ``null``: output the missing entries as JSON null.
        - ``drop``: remove the property from the feature. This applies to each
          feature individually so that features may have different properties.
        - ``keep``: output the missing entries as NaN.

        If the GeoDataFrame has a defined CRS, its definition will be included
        in the output unless it is equal to WGS84 (default GeoJSON CRS) or not
        possible to represent in the URN OGC format, or unless ``to_wgs84=True``
        is specified.

        Examples
        --------
        >>> from shapely.geometry import Point
        >>> d = {'col1': ['name1', 'name2'], 'geometry': [Point(1, 2), Point(2, 1)]}
        >>> gdf = geopandas.GeoDataFrame(d, crs="EPSG:3857")
        >>> gdf
            col1     geometry
        0  name1  POINT (1 2)
        1  name2  POINT (2 1)

        >>> gdf.to_json()
        '{"type": "FeatureCollection", "features": [{"id": "0", "type": "Feature", \
"properties": {"col1": "name1"}, "geometry": {"type": "Point", "coordinates": [1.0,\
 2.0]}}, {"id": "1", "type": "Feature", "properties": {"col1": "name2"}, "geometry"\
: {"type": "Point", "coordinates": [2.0, 1.0]}}], "crs": {"type": "name", "properti\
es": {"name": "urn:ogc:def:crs:EPSG::3857"}}}'

        Alternatively, you can write GeoJSON to file:

        >>> gdf.to_file(path, driver="GeoJSON")  # doctest: +SKIP

        See Also
        --------
        GeoDataFrame.to_file : write GeoDataFrame to file

        """
        if to_wgs84:
            if self.crs:
                df = self.to_crs(epsg=4326)
            else:
                raise ValueError(
                    "CRS is not set. Cannot re-project to WGS84 (EPSG:4326)."
                )
        else:
            df = self

        geo = df.to_geo_dict(na=na, show_bbox=show_bbox, drop_id=drop_id)

        # if the geometry is not in WGS84, include CRS in the JSON
        if df.crs is not None and not df.crs.equals("epsg:4326"):
            auth_crsdef = self.crs.to_authority()
            allowed_authorities = ["EDCS", "EPSG", "OGC", "SI", "UCUM"]

            if auth_crsdef is None or auth_crsdef[0] not in allowed_authorities:
                warnings.warn(
                    "GeoDataFrame's CRS is not representable in URN OGC "
                    "format. Resulting JSON will contain no CRS information.",
                    stacklevel=2,
                )
            else:
                authority, code = auth_crsdef
                ogc_crs = f"urn:ogc:def:crs:{authority}::{code}"
                geo["crs"] = {"type": "name", "properties": {"name": ogc_crs}}

        return json.dumps(geo, **kwargs)

    @property
    def __geo_interface__(self) -> dict:
        """Returns a ``GeoDataFrame`` as a python feature collection.

        Implements the `geo_interface`. The returned python data structure
        represents the ``GeoDataFrame`` as a GeoJSON-like
        ``FeatureCollection``.

        This differs from :meth:`to_geo_dict` only in that it is a property with
        default args instead of a method.

        CRS of the dataframe is not passed on to the output, unlike
        :meth:`~GeoDataFrame.to_json()`.

        Examples
        --------
        >>> from shapely.geometry import Point
        >>> d = {'col1': ['name1', 'name2'], 'geometry': [Point(1, 2), Point(2, 1)]}
        >>> gdf = geopandas.GeoDataFrame(d, crs="EPSG:4326")
        >>> gdf
            col1     geometry
        0  name1  POINT (1 2)
        1  name2  POINT (2 1)

        >>> gdf.__geo_interface__
        {'type': 'FeatureCollection', 'features': [{'id': '0', 'type': 'Feature', \
'properties': {'col1': 'name1'}, 'geometry': {'type': 'Point', 'coordinates': (1.0\
, 2.0)}, 'bbox': (1.0, 2.0, 1.0, 2.0)}, {'id': '1', 'type': 'Feature', 'properties\
': {'col1': 'name2'}, 'geometry': {'type': 'Point', 'coordinates': (2.0, 1.0)}, 'b\
box': (2.0, 1.0, 2.0, 1.0)}], 'bbox': (1.0, 1.0, 2.0, 2.0)}
        """
        return self.to_geo_dict(na="null", show_bbox=True, drop_id=False)

    def iterfeatures(
        self, na: str = "null", show_bbox: bool = False, drop_id: bool = False
    ) -> typing.Generator[dict]:
        """Return an iterator that yields feature dictionaries that comply with
        __geo_interface__.

        Parameters
        ----------
        na : str, optional
            Options are {'null', 'drop', 'keep'}, default 'null'.
            Indicates how to output missing (NaN) values in the GeoDataFrame

            - null: output the missing entries as JSON null
            - drop: remove the property from the feature. This applies to each feature \
individually so that features may have different properties
            - keep: output the missing entries as NaN

        show_bbox : bool, optional
            Include bbox (bounds) in the geojson. Default False.
        drop_id : bool, default: False
            Whether to retain the index of the GeoDataFrame as the id property
            in the generated GeoJSON. Default is False, but may want True
            if the index is just arbitrary row numbers.

        Examples
        --------
        >>> from shapely.geometry import Point
        >>> d = {'col1': ['name1', 'name2'], 'geometry': [Point(1, 2), Point(2, 1)]}
        >>> gdf = geopandas.GeoDataFrame(d, crs="EPSG:4326")
        >>> gdf
            col1     geometry
        0  name1  POINT (1 2)
        1  name2  POINT (2 1)

        >>> feature = next(gdf.iterfeatures())
        >>> feature
        {'id': '0', 'type': 'Feature', 'properties': {'col1': 'name1'}, 'geometry': {\
'type': 'Point', 'coordinates': (1.0, 2.0)}}
        """
        if na not in ["null", "drop", "keep"]:
            raise ValueError(f"Unknown na method {na}")

        ids = np.asarray(self.index)
        geometries = np.asarray(self.geometry)

        if not self.columns.is_unique:
            raise ValueError("GeoDataFrame cannot contain duplicated column names.")

        properties_cols = self.columns.drop(self._geometry_column_name)

        if len(properties_cols) > 0:
            # convert to object to get python scalars.
            properties_cols = self[properties_cols]
            properties = properties_cols.astype(object)
            na_mask = pd.isna(properties_cols).values

            if na == "null":
                properties[na_mask] = None

            for i, row in enumerate(properties.values):
                geom = geometries[i]

                if na == "drop":
                    na_mask_row = na_mask[i]
                    properties_items = {
                        k: v
                        for k, v, na in zip(properties_cols, row, na_mask_row)
                        if not na
                    }
                else:
                    properties_items = dict(zip(properties_cols, row))

                if drop_id:
                    feature = {}
                else:
                    feature = {"id": str(ids[i])}

                feature["type"] = "Feature"
                feature["properties"] = properties_items
                feature["geometry"] = mapping(geom) if geom else None

                if show_bbox:
                    feature["bbox"] = geom.bounds if geom else None

                yield feature

        else:
            for fid, geom in zip(ids, geometries):
                if drop_id:
                    feature = {}
                else:
                    feature = {"id": str(fid)}

                feature["type"] = "Feature"
                feature["properties"] = {}
                feature["geometry"] = mapping(geom) if geom else None

                if show_bbox:
                    feature["bbox"] = geom.bounds if geom else None

                yield feature

    def to_geo_dict(
        self, na: str | None = "null", show_bbox: bool = False, drop_id: bool = False
    ) -> dict:
        """Return a python feature collection representation of the GeoDataFrame
        as a dictionary with a list of features based on the ``__geo_interface__``
        GeoJSON-like specification.

        Parameters
        ----------
        na : str, optional
            Options are {'null', 'drop', 'keep'}, default 'null'.
            Indicates how to output missing (NaN) values in the GeoDataFrame

            - null: output the missing entries as JSON null
            - drop: remove the property from the feature. This applies to each feature \
individually so that features may have different properties
            - keep: output the missing entries as NaN

        show_bbox : bool, optional
            Include bbox (bounds) in the geojson. Default False.
        drop_id : bool, default: False
            Whether to retain the index of the GeoDataFrame as the id property
            in the generated dictionary. Default is False, but may want True
            if the index is just arbitrary row numbers.

        Examples
        --------
        >>> from shapely.geometry import Point
        >>> d = {'col1': ['name1', 'name2'], 'geometry': [Point(1, 2), Point(2, 1)]}
        >>> gdf = geopandas.GeoDataFrame(d)
        >>> gdf
            col1     geometry
        0  name1  POINT (1 2)
        1  name2  POINT (2 1)

        >>> gdf.to_geo_dict()
        {'type': 'FeatureCollection', 'features': [{'id': '0', 'type': 'Feature', '\
properties': {'col1': 'name1'}, 'geometry': {'type': 'Point', 'coordinates': (1.0, \
2.0)}}, {'id': '1', 'type': 'Feature', 'properties': {'col1': 'name2'}, 'geometry':\
 {'type': 'Point', 'coordinates': (2.0, 1.0)}}]}

        See Also
        --------
        GeoDataFrame.to_json : return a GeoDataFrame as a GeoJSON string

        """
        geo = {
            "type": "FeatureCollection",
            "features": list(
                self.iterfeatures(na=na, show_bbox=show_bbox, drop_id=drop_id)
            ),
        }

        if show_bbox:
            geo["bbox"] = tuple(self.total_bounds.tolist())  # tolist to avoid np dtypes

        return geo

    def to_wkb(self, hex: bool = False, **kwargs) -> pd.DataFrame:
        """
        Encode all geometry columns in the GeoDataFrame to WKB.

        Parameters
        ----------
        hex : bool
            If true, export the WKB as a hexadecimal string.
            The default is to return a binary bytes object.
        kwargs
            Additional keyword args will be passed to
            :func:`shapely.to_wkb`.

        Returns
        -------
        DataFrame
            geometry columns are encoded to WKB
        """
        df = DataFrame(self.copy())

        # Encode all geometry columns to WKB
        for col in df.columns[df.dtypes == "geometry"]:
            df[col] = to_wkb(df[col].values, hex=hex, **kwargs)

        return df

    def to_wkt(self, **kwargs) -> pd.DataFrame:
        """
        Encode all geometry columns in the GeoDataFrame to WKT.

        Parameters
        ----------
        kwargs
            Keyword args will be passed to :func:`shapely.to_wkt`.

        Returns
        -------
        DataFrame
            geometry columns are encoded to WKT
        """
        df = DataFrame(self.copy())

        # Encode all geometry columns to WKT
        for col in df.columns[df.dtypes == "geometry"]:
            df[col] = to_wkt(df[col].values, **kwargs)

        return df

    def to_arrow(
        self,
        *,
        index: bool | None = None,
        geometry_encoding: PARQUET_GEOMETRY_ENCODINGS = "WKB",
        interleaved: bool = True,
        include_z: bool | None = None,
    ):
        """Encode a GeoDataFrame to GeoArrow format.

        See https://geoarrow.org/ for details on the GeoArrow specification.

        This function returns a generic Arrow data object implementing
        the `Arrow PyCapsule Protocol`_ (i.e. having an ``__arrow_c_stream__``
        method). This object can then be consumed by your Arrow implementation
        of choice that supports this protocol.

        .. _Arrow PyCapsule Protocol: https://arrow.apache.org/docs/format/CDataInterface/PyCapsuleInterface.html

        .. versionadded:: 1.0

        Parameters
        ----------
        index : bool, default None
            If ``True``, always include the dataframe's index(es) as columns
            in the file output.
            If ``False``, the index(es) will not be written to the file.
            If ``None``, the index(ex) will be included as columns in the file
            output except `RangeIndex` which is stored as metadata only.
        geometry_encoding : {'WKB', 'geoarrow' }, default 'WKB'
            The GeoArrow encoding to use for the data conversion.
        interleaved : bool, default True
            Only relevant for 'geoarrow' encoding. If True, the geometries'
            coordinates are interleaved in a single fixed size list array.
            If False, the coordinates are stored as separate arrays in a
            struct type.
        include_z : bool, default None
            Only relevant for 'geoarrow' encoding (for WKB, the dimensionality
            of the individual geometries is preserved).
            If False, return 2D geometries. If True, include the third dimension
            in the output (if a geometry has no third dimension, the z-coordinates
            will be NaN). By default, will infer the dimensionality from the
            input geometries. Note that this inference can be unreliable with
            empty geometries (for a guaranteed result, it is recommended to
            specify the keyword).

        Returns
        -------
        ArrowTable
            A generic Arrow table object with geometry columns encoded to
            GeoArrow.

        Examples
        --------
        >>> from shapely.geometry import Point
        >>> data = {'col1': ['name1', 'name2'], 'geometry': [Point(1, 2), Point(2, 1)]}
        >>> gdf = geopandas.GeoDataFrame(data)
        >>> gdf
            col1     geometry
        0  name1  POINT (1 2)
        1  name2  POINT (2 1)

        >>> arrow_table = gdf.to_arrow()
        >>> arrow_table
        <geopandas.io._geoarrow.ArrowTable object at ...>

        The returned data object needs to be consumed by a library implementing
        the Arrow PyCapsule Protocol. For example, wrapping the data as a
        pyarrow.Table (requires pyarrow >= 14.0):

        >>> import pyarrow as pa
        >>> table = pa.table(arrow_table)
        >>> table
        pyarrow.Table
        col1: string
        geometry: binary
        ----
        col1: [["name1","name2"]]
        geometry: [[0101000000000000000000F03F0000000000000040,\
01010000000000000000000040000000000000F03F]]

        """
        from geopandas.io._geoarrow import ArrowTable, geopandas_to_arrow

        table, _ = geopandas_to_arrow(
            self,
            index=index,
            geometry_encoding=geometry_encoding,
            interleaved=interleaved,
            include_z=include_z,
        )
        return ArrowTable(table)

    def to_parquet(
        self,
        path: os.PathLike | typing.IO,
        index: bool | None = None,
        compression: str = "snappy",
        geometry_encoding: PARQUET_GEOMETRY_ENCODINGS = "WKB",
        write_covering_bbox: bool = False,
        schema_version: SUPPORTED_VERSIONS_LITERAL | None = None,
        **kwargs,
    ) -> None:
        """Write a GeoDataFrame to the Parquet format.

        By default, all geometry columns present are serialized to WKB format
        in the file.

        Requires 'pyarrow'.

        .. versionadded:: 0.8

        Parameters
        ----------
        path : str, path object
        index : bool, default None
            If ``True``, always include the dataframe's index(es) as columns
            in the file output.
            If ``False``, the index(es) will not be written to the file.
            If ``None``, the index(ex) will be included as columns in the file
            output except `RangeIndex` which is stored as metadata only.
        compression : {'snappy', 'gzip', 'brotli', 'lz4', 'zstd', None}, \
default 'snappy'
            Name of the compression to use. Use ``None`` for no compression.
        geometry_encoding : {'WKB', 'geoarrow'}, default 'WKB'
            The encoding to use for the geometry columns. Defaults to "WKB"
            for maximum interoperability. Specify "geoarrow" to use one of the
            native GeoArrow-based single-geometry type encodings.
            Note: the "geoarrow" option is part of the newer GeoParquet 1.1
            specification, should be considered as experimental, and may not
            be supported by all readers.
        write_covering_bbox : bool, default False
            Writes the bounding box column for each row entry with column
            name 'bbox'. Writing a bbox column can be computationally
            expensive, but allows you to specify a `bbox` in :
            func:`read_parquet` for filtered reading.
            Note: this bbox column is part of the newer GeoParquet 1.1
            specification and should be considered as experimental. While
            writing the column is backwards compatible, using it for filtering
            may not be supported by all readers.
        schema_version : {'0.1.0', '0.4.0', '1.0.0', '1.1.0', None}
            GeoParquet specification version; if not provided, will default to
            latest supported stable version (1.0.0).
        kwargs
            Additional keyword arguments passed to :func:`pyarrow.parquet.write_table`.

        Examples
        --------
        >>> gdf.to_parquet('data.parquet')  # doctest: +SKIP

        See Also
        --------
        GeoDataFrame.to_feather : write GeoDataFrame to feather
        GeoDataFrame.to_file : write GeoDataFrame to file
        """
        # Accept engine keyword for compatibility with pandas.DataFrame.to_parquet
        # The only engine currently supported by GeoPandas is pyarrow, so no
        # other engine should be specified.
        engine = kwargs.pop("engine", "auto")
        if engine not in ("auto", "pyarrow"):
            raise ValueError(
                "GeoPandas only supports using pyarrow as the engine for "
                f"to_parquet: {engine!r} passed instead."
            )

        from geopandas.io.arrow import _to_parquet

        _to_parquet(
            self,
            path,
            compression=compression,
            geometry_encoding=geometry_encoding,
            index=index,
            schema_version=schema_version,
            write_covering_bbox=write_covering_bbox,
            **kwargs,
        )

    def to_feather(
        self,
        path: os.PathLike,
        index: bool | None = None,
        compression: str | None = None,
        schema_version: SUPPORTED_VERSIONS_LITERAL | None = None,
        **kwargs,
    ):
        """Write a GeoDataFrame to the Feather format.

        Any geometry columns present are serialized to WKB format in the file.

        Requires 'pyarrow' >= 0.17.

        .. versionadded:: 0.8

        Parameters
        ----------
        path : str, path object
        index : bool, default None
            If ``True``, always include the dataframe's index(es) as columns
            in the file output.
            If ``False``, the index(es) will not be written to the file.
            If ``None``, the index(ex) will be included as columns in the file
            output except `RangeIndex` which is stored as metadata only.
        compression : {'zstd', 'lz4', 'uncompressed'}, optional
            Name of the compression to use. Use ``"uncompressed"`` for no
            compression. By default uses LZ4 if available, otherwise uncompressed.
        schema_version : {'0.1.0', '0.4.0', '1.0.0', '1.1.0' None}
            GeoParquet specification version; if not provided will default to
            latest supported stable version (1.0.0).
        kwargs
            Additional keyword arguments passed to
            :func:`pyarrow.feather.write_feather`.

        Examples
        --------
        >>> gdf.to_feather('data.feather')  # doctest: +SKIP

        See Also
        --------
        GeoDataFrame.to_parquet : write GeoDataFrame to parquet
        GeoDataFrame.to_file : write GeoDataFrame to file
        """
        from geopandas.io.arrow import _to_feather

        _to_feather(
            self,
            path,
            index=index,
            compression=compression,
            schema_version=schema_version,
            **kwargs,
        )

    def to_file(
        self,
        filename: os.PathLike | typing.IO,
        driver: str | None = None,
        schema: dict | None = None,
        index: bool | None = None,
        **kwargs,
    ):
        """Write the ``GeoDataFrame`` to a file.

        By default, an ESRI shapefile is written, but any OGR data source
        supported by Pyogrio or Fiona can be written. A dictionary of supported OGR
        providers is available via:

        >>> import pyogrio
        >>> pyogrio.list_drivers()  # doctest: +SKIP

        Parameters
        ----------
        filename : string
            File path or file handle to write to. The path may specify a
            GDAL VSI scheme.
        driver : string, default None
            The OGR format driver used to write the vector file.
            If not specified, it attempts to infer it from the file extension.
            If no extension is specified, it saves ESRI Shapefile to a folder.
        schema : dict, default None
            If specified, the schema dictionary is passed to Fiona to
            better control how the file is written. If None, GeoPandas
            will determine the schema based on each column's dtype.
            Not supported for the "pyogrio" engine.
        index : bool, default None
            If True, write index into one or more columns (for MultiIndex).
            Default None writes the index into one or more columns only if
            the index is named, is a MultiIndex, or has a non-integer data
            type. If False, no index is written.

            .. versionadded:: 0.7
                Previously the index was not written.
        mode : string, default 'w'
            The write mode, 'w' to overwrite the existing file and 'a' to append.
            Not all drivers support appending. The drivers that support appending
            are listed in fiona.supported_drivers or
            https://github.com/Toblerity/Fiona/blob/master/fiona/drvsupport.py
        crs : pyproj.CRS, default None
            If specified, the CRS is passed to Fiona to
            better control how the file is written. If None, GeoPandas
            will determine the crs based on crs df attribute.
            The value can be anything accepted
            by :meth:`pyproj.CRS.from_user_input() <pyproj.crs.CRS.from_user_input>`,
            such as an authority string (eg "EPSG:4326") or a WKT string. The keyword
            is not supported for the "pyogrio" engine.
        engine : str, "pyogrio" or "fiona"
            The underlying library that is used to write the file. Currently, the
            supported options are "pyogrio" and "fiona". Defaults to "pyogrio" if
            installed, otherwise tries "fiona".
        metadata : dict[str, str], default None
            Optional metadata to be stored in the file. Keys and values must be
            strings. Supported only for "GPKG" driver.
        **kwargs :
            Keyword args to be passed to the engine, and can be used to write
            to multi-layer data, store data within archives (zip files), etc.
            In case of the "pyogrio" engine, the keyword arguments are passed to
            `pyogrio.write_dataframe`. In case of the "fiona" engine, the keyword
            arguments are passed to fiona.open`. For more information on possible
            keywords, type: ``import pyogrio; help(pyogrio.write_dataframe)``.

        Notes
        -----
        The format drivers will attempt to detect the encoding of your data, but
        may fail. In this case, the proper encoding can be specified explicitly
        by using the encoding keyword parameter, e.g. ``encoding='utf-8'``.

        See Also
        --------
        GeoSeries.to_file
        GeoDataFrame.to_postgis : write GeoDataFrame to PostGIS database
        GeoDataFrame.to_parquet : write GeoDataFrame to parquet
        GeoDataFrame.to_feather : write GeoDataFrame to feather

        Examples
        --------
        >>> gdf.to_file('dataframe.shp')  # doctest: +SKIP

        >>> gdf.to_file('dataframe.gpkg', driver='GPKG', layer='name')  # doctest: +SKIP

        >>> gdf.to_file('dataframe.geojson', driver='GeoJSON')  # doctest: +SKIP

        With selected drivers you can also append to a file with `mode="a"`:

        >>> gdf.to_file('dataframe.shp', mode="a")  # doctest: +SKIP

        Using the engine-specific keyword arguments it is possible to e.g. create a
        spatialite file with a custom layer name:

        >>> gdf.to_file(
        ...     'dataframe.sqlite', driver='SQLite', spatialite=True, layer='test'
        ... )  # doctest: +SKIP

        """
        from geopandas.io.file import _to_file

        _to_file(self, filename, driver, schema, index, **kwargs)

    @typing.overload
    def set_crs(
        self,
        crs: Any | None = ...,
        epsg: int | None = ...,
        inplace: Literal[True] = ...,
        allow_override: bool = ...,
    ) -> None: ...

    @typing.overload
    def set_crs(
        self,
        crs: Any | None = ...,
        epsg: int | None = ...,
        inplace: Literal[False] = ...,
        allow_override: bool = ...,
    ) -> GeoDataFrame: ...

    def set_crs(
        self,
        crs: Any | None = None,
        epsg: int | None = None,
        inplace: bool = False,
        allow_override: bool = False,
    ) -> GeoDataFrame | None:
        """
        Set the Coordinate Reference System (CRS) of the ``GeoDataFrame``.

        If there are multiple geometry columns within the GeoDataFrame, only
        the CRS of the active geometry column is set.

        Pass ``None`` to remove CRS from the active geometry column.

        Notes
        -----
        The underlying geometries are not transformed to this CRS. To
        transform the geometries to a new CRS, use the ``to_crs`` method.

        Parameters
        ----------
        crs : pyproj.CRS | None, optional
            The value can be anything accepted
            by :meth:`pyproj.CRS.from_user_input() <pyproj.crs.CRS.from_user_input>`,
            such as an authority string (eg "EPSG:4326") or a WKT string.
        epsg : int, optional
            EPSG code specifying the projection.
        inplace : bool, default False
            If True, the CRS of the GeoDataFrame will be changed in place
            (while still returning the result) instead of making a copy of
            the GeoDataFrame.
        allow_override : bool, default False
            If the the GeoDataFrame already has a CRS, allow to replace the
            existing CRS, even when both are not equal.

        Examples
        --------
        >>> from shapely.geometry import Point
        >>> d = {'col1': ['name1', 'name2'], 'geometry': [Point(1, 2), Point(2, 1)]}
        >>> gdf = geopandas.GeoDataFrame(d)
        >>> gdf
            col1     geometry
        0  name1  POINT (1 2)
        1  name2  POINT (2 1)

        Setting CRS to a GeoDataFrame without one:

        >>> gdf.crs is None
        True

        >>> gdf = gdf.set_crs('epsg:3857')
        >>> gdf.crs  # doctest: +SKIP
        <Projected CRS: EPSG:3857>
        Name: WGS 84 / Pseudo-Mercator
        Axis Info [cartesian]:
        - X[east]: Easting (metre)
        - Y[north]: Northing (metre)
        Area of Use:
        - name: World - 85°S to 85°N
        - bounds: (-180.0, -85.06, 180.0, 85.06)
        Coordinate Operation:
        - name: Popular Visualisation Pseudo-Mercator
        - method: Popular Visualisation Pseudo Mercator
        Datum: World Geodetic System 1984
        - Ellipsoid: WGS 84
        - Prime Meridian: Greenwich

        Overriding existing CRS:

        >>> gdf = gdf.set_crs(4326, allow_override=True)

        Without ``allow_override=True``, ``set_crs`` returns an error if you try to
        override CRS.

        See Also
        --------
        GeoDataFrame.to_crs : re-project to another CRS

        """
        if not inplace:
            df = self.copy()
        else:
            df = self
        df.geometry = df.geometry.set_crs(
            crs=crs, epsg=epsg, allow_override=allow_override, inplace=True
        )
        return df

    @typing.overload
    def to_crs(
        self,
        crs: Any | None = ...,
        epsg: int | None = ...,
        inplace: Literal[False] = ...,
    ) -> GeoDataFrame: ...

    @typing.overload
    def to_crs(
        self,
        crs: Any | None = ...,
        epsg: int | None = ...,
        inplace: Literal[True] = ...,
    ) -> None: ...

    def to_crs(
        self,
        crs: Any | None = None,
        epsg: int | None = None,
        inplace: bool = False,
    ) -> GeoDataFrame | None:
        """Transform geometries to a new coordinate reference system.

        Transform all geometries in an active geometry column to a different coordinate
        reference system.  The ``crs`` attribute on the current GeoSeries must
        be set.  Either ``crs`` or ``epsg`` may be specified for output.

        This method will transform all points in all objects. It has no notion
        of projecting entire geometries.  All segments joining points are
        assumed to be lines in the current projection, not geodesics. Objects
        crossing the dateline (or other projection boundary) will have
        undesirable behavior.

        Parameters
        ----------
        crs : pyproj.CRS, optional if `epsg` is specified
            The value can be anything accepted by
            :meth:`pyproj.CRS.from_user_input() <pyproj.crs.CRS.from_user_input>`,
            such as an authority string (eg "EPSG:4326") or a WKT string.
        epsg : int, optional if `crs` is specified
            EPSG code specifying output projection.
        inplace : bool, optional, default: False
            Whether to return a new GeoDataFrame or do the transformation in
            place.

        Returns
        -------
        GeoDataFrame

        Examples
        --------
        >>> from shapely.geometry import Point
        >>> d = {'col1': ['name1', 'name2'], 'geometry': [Point(1, 2), Point(2, 1)]}
        >>> gdf = geopandas.GeoDataFrame(d, crs=4326)
        >>> gdf
            col1     geometry
        0  name1  POINT (1 2)
        1  name2  POINT (2 1)
        >>> gdf.crs  # doctest: +SKIP
        <Geographic 2D CRS: EPSG:4326>
        Name: WGS 84
        Axis Info [ellipsoidal]:
        - Lat[north]: Geodetic latitude (degree)
        - Lon[east]: Geodetic longitude (degree)
        Area of Use:
        - name: World
        - bounds: (-180.0, -90.0, 180.0, 90.0)
        Datum: World Geodetic System 1984
        - Ellipsoid: WGS 84
        - Prime Meridian: Greenwich

        >>> gdf = gdf.to_crs(3857)
        >>> gdf
            col1                       geometry
        0  name1  POINT (111319.491 222684.209)
        1  name2  POINT (222638.982 111325.143)
        >>> gdf.crs  # doctest: +SKIP
        <Projected CRS: EPSG:3857>
        Name: WGS 84 / Pseudo-Mercator
        Axis Info [cartesian]:
        - X[east]: Easting (metre)
        - Y[north]: Northing (metre)
        Area of Use:
        - name: World - 85°S to 85°N
        - bounds: (-180.0, -85.06, 180.0, 85.06)
        Coordinate Operation:
        - name: Popular Visualisation Pseudo-Mercator
        - method: Popular Visualisation Pseudo Mercator
        Datum: World Geodetic System 1984
        - Ellipsoid: WGS 84
        - Prime Meridian: Greenwich

        See Also
        --------
        GeoDataFrame.set_crs : assign CRS without re-projection
        """
        if inplace:
            df = self
        else:
            df = self.copy()
        geom = df.geometry.to_crs(crs=crs, epsg=epsg)
        df.geometry = geom
        if not inplace:
            return df

    def estimate_utm_crs(self, datum_name: str = "WGS 84") -> CRS:
        """Return the estimated UTM CRS based on the bounds of the dataset.

        .. versionadded:: 0.9

        Parameters
        ----------
        datum_name : str, optional
            The name of the datum to use in the query. Default is WGS 84.

        Returns
        -------
        pyproj.CRS

        Examples
        --------
        >>> import geodatasets
        >>> df = geopandas.read_file(
        ...     geodatasets.get_path("geoda.chicago_health")
        ... )
        >>> df.estimate_utm_crs()  # doctest: +SKIP
        <Derived Projected CRS: EPSG:32616>
        Name: WGS 84 / UTM zone 16N
        Axis Info [cartesian]:
        - E[east]: Easting (metre)
        - N[north]: Northing (metre)
        Area of Use:
        - name: Between 90°W and 84°W, northern hemisphere between equator and 84°N...
        - bounds: (-90.0, 0.0, -84.0, 84.0)
        Coordinate Operation:
        - name: UTM zone 16N
        - method: Transverse Mercator
        Datum: World Geodetic System 1984 ensemble
        - Ellipsoid: WGS 84
        - Prime Meridian: Greenwich
        """
        return self.geometry.estimate_utm_crs(datum_name=datum_name)

    def __getitem__(self, key):
        """
        If the result is a column containing only 'geometry', return a
        GeoSeries. If it's a DataFrame with any columns of GeometryDtype,
        return a GeoDataFrame.
        """
        result = super().__getitem__(key)
        # Custom logic to avoid waiting for pandas GH51895
        # result is not geometry dtype for multi-indexes
        if (
            pd.api.types.is_scalar(key)
            and key == ""
            and isinstance(self.columns, pd.MultiIndex)
            and isinstance(result, Series)
            and not is_geometry_type(result)
        ):
            loc = self.columns.get_loc(key)
            # squeeze stops multilevel columns from returning a gdf
            result = self.iloc[:, loc].squeeze(axis="columns")
        geo_col = self._geometry_column_name
        if isinstance(result, Series) and isinstance(result.dtype, GeometryDtype):
            result.__class__ = GeoSeries
        elif isinstance(result, DataFrame):
            if (result.dtypes == "geometry").sum() > 0:
                result.__class__ = type(self)
                if geo_col in result:
                    result._geometry_column_name = geo_col
            else:
                result.__class__ = DataFrame
        return result

    def __delitem__(self, key) -> None:
        """If the last geometry column is removed, downcast to a dataframe."""
        super().__delitem__(key)
        if (self.dtypes == "geometry").sum() == 0:
            self.__class__ = DataFrame

    def _persist_old_default_geometry_colname(self) -> None:
        """Persist the default geometry column name of 'geometry' temporarily for
        backwards compatibility.
        """
        # self.columns check required to avoid this warning in __init__
        if self._geometry_column_name is None and "geometry" not in self.columns:
            msg = (
                "You are adding a column named 'geometry' to a GeoDataFrame "
                "constructed without an active geometry column. Currently, "
                "this automatically sets the active geometry column to 'geometry' "
                "but in the future that will no longer happen. Instead, either "
                "provide geometry to the GeoDataFrame constructor "
                "(GeoDataFrame(... geometry=GeoSeries()) or use "
                "`set_geometry('geometry')` "
                "to explicitly set the active geometry column."
            )
            warnings.warn(msg, category=FutureWarning, stacklevel=3)
            self._geometry_column_name = "geometry"

    def __setitem__(self, key, value):
        """Overridden to preserve CRS of GeometryArray.

        Important for cases like
        df['geometry'] = [geom... for geom in df.geometry]
        """
        if not pd.api.types.is_list_like(key) and (
            key == self._geometry_column_name
            or (key == "geometry" and self._geometry_column_name is None)
        ):
            if pd.api.types.is_scalar(value) or isinstance(value, BaseGeometry):
                value = [value] * self.shape[0]

            crs = getattr(self, "crs", None)
            # if we don't have a GeoDataFrame yet and there is a column named crs,
            # don't try to use that as a crs
            if isinstance(crs, pd.Series | pd.DataFrame):
                crs = None
            try:
                value = _ensure_geometry(value, crs=crs)
            except TypeError:
                warnings.warn(
                    "Geometry column does not contain geometry.",
                    stacklevel=2,
                )
            else:
                if key == "geometry":
                    self._persist_old_default_geometry_colname()
        super().__setitem__(key, value)

    #
    # Implement pandas methods
    #
    @doc(pd.DataFrame)
    def copy(self, deep: bool = True) -> GeoDataFrame:
        copied = super().copy(deep=deep)
        if type(copied) is pd.DataFrame:
            copied.__class__ = type(self)
            copied._geometry_column_name = self._geometry_column_name
        return copied

    @doc(pd.DataFrame)
    def apply(
        self,
        func,
        axis=0,
        raw: bool = False,
        result_type=None,
        args=(),
        **kwargs,
    ):
        result = super().apply(
            func, axis=axis, raw=raw, result_type=result_type, args=args, **kwargs
        )
        # Reconstruct gdf if it was lost by apply
        if (
            isinstance(result, DataFrame)
            and self._geometry_column_name in result.columns
        ):
            # axis=1 apply will split GeometryDType to object, try and cast back
            try:
                result = result.set_geometry(self._geometry_column_name)
            except TypeError:
                pass
            else:
                if self.crs is not None and result.crs is None:
                    result.set_crs(self.crs, inplace=True)
        elif isinstance(result, Series) and result.dtype == "object":
            # Try reconstruct series GeometryDtype if lost by apply
            # If all none and object dtype assert list of nones is more likely
            # intended than list of null geometry.
            if not result.isna().all():
                try:
                    # not enough info about func to preserve CRS
                    result = _ensure_geometry(result)

                except (TypeError, shapely.errors.GeometryTypeError):
                    pass

        return result

    @classmethod
    def _geodataframe_constructor_with_fallback(
        cls, *args, **kwargs
    ) -> pd.DataFrame | GeoDataFrame:
        """A flexible constructor for GeoDataFrame._constructor, which falls back
        to returning a DataFrame (if a certain operation does not preserve the
        geometry column).
        """  # noqa: D401
        df = cls(*args, **kwargs)

        geometry_cols_mask = df.dtypes == "geometry"

        if len(geometry_cols_mask) == 0 or geometry_cols_mask.sum() == 0:
            df = pd.DataFrame(df)

        return df

    @property
    def _constructor(self) -> DataFrame | GeoDataFrame:
        return self._geodataframe_constructor_with_fallback

    def _constructor_from_mgr(self, mgr, axes) -> DataFrame | GeoDataFrame:
        # replicate _geodataframe_constructor_with_fallback behaviour
        # unless safe to skip
        if not any(isinstance(block.dtype, GeometryDtype) for block in mgr.blocks):
            return self._geodataframe_constructor_with_fallback(
                pd.DataFrame._from_mgr(mgr, axes)
            )
        gdf = self._from_mgr(mgr, axes)
        # _from_mgr doesn't preserve metadata (expect __finalize__ to be called)
        # still need to mimic __init__ behaviour with geometry=None
        if (gdf.columns == "geometry").sum() == 1:  # only if "geometry" is single col
            gdf._geometry_column_name = "geometry"
        return gdf

    @property
    def _constructor_sliced(self) -> Series | GeoSeries:
        def _geodataframe_constructor_sliced(*args, **kwargs):
            """A specialized (Geo)Series constructor which can fall back to a
            Series if a certain operation does not produce geometries.

            Note:

            - We only return a GeoSeries if the data is actually of geometry
              dtype (and so we don't try to convert geometry objects such as
              the normal GeoSeries(..) constructor does with `_ensure_geometry`).
            - When we get here from obtaining a row or column from a
              GeoDataFrame, the goal is to only return a GeoSeries for a
              geometry column, and not return a GeoSeries for a row that happened
              to come from a DataFrame with only geometry dtype columns (and
              thus could have a geometry dtype). Therefore, we don't return a
              GeoSeries if we are sure we are in a row selection case (by
              checking the identity of the index)
            """  # noqa: D401
            srs = pd.Series(*args, **kwargs)
            is_row_proxy = srs.index.is_(self.columns)
            if is_geometry_type(srs) and not is_row_proxy:
                srs = GeoSeries(srs)
            return srs

        return _geodataframe_constructor_sliced

    def _constructor_sliced_from_mgr(self, mgr, axes) -> Series | GeoSeries:
        is_row_proxy = mgr.index.is_(self.columns)

        if isinstance(mgr.blocks[0].dtype, GeometryDtype) and not is_row_proxy:
            return GeoSeries._from_mgr(mgr, axes)
        return Series._from_mgr(mgr, axes)

    def __finalize__(
        self, other, method: str | None = None, **kwargs
    ) -> GeoDataFrame | GeoSeries:
        """Propagate metadata from other to self."""
        self = super().__finalize__(other, method=method, **kwargs)  # noqa: PLW0642

        # merge operation: using metadata of the left object
        if method == "merge":
            # pandas-dev/pandas#60357 : merge/concat use input_objs
            if PANDAS_GE_30:
                # other is a types.SimpleNameSpace
                left_obj = other.input_objs[0]
            else:
                # other is a _MergeOperation
                left_obj = other.left
            for name in self._metadata:
                object.__setattr__(self, name, getattr(left_obj, name, None))
        # concat operation: using metadata of the first object
        elif method == "concat":
            # pandas-dev/pandas#60357 : merge/concat use input_objs
            if PANDAS_GE_30:
                first_obj = other.input_objs[0]
            else:
                first_obj = other.objs[0]
            for name in self._metadata:
                object.__setattr__(self, name, getattr(first_obj, name, None))

            if (
                self.columns.nlevels == 1
                and (self.columns == self._geometry_column_name).sum() > 1
            ) or (
                self.columns.nlevels > 1
                and (
                    self.columns.get_level_values(0) == self._geometry_column_name
                ).sum()
                > 1
            ):
                raise ValueError(
                    "Concat operation has resulted in multiple columns using "
                    f"the geometry column name '{self._geometry_column_name}'.\n"
                    "Please ensure this column from the first DataFrame is not "
                    "repeated."
                )
        elif method == "unstack":
            # unstack adds multiindex columns and reshapes data.
            # it never makes sense to retain geometry column
            self._geometry_column_name = None
            self._crs = None
        return self

    def dissolve(
        self,
        by: str | None = None,
        aggfunc="first",
        as_index: bool = True,
        level=None,
        sort: bool = True,
        observed: bool = False,
        dropna: bool = True,
        method: Literal["unary", "coverage", "disjoint_subset"] = "unary",
        grid_size: float | None = None,
        **kwargs,
    ) -> GeoDataFrame:
        """
        Dissolve geometries within `groupby` into single observation.
        This is accomplished by applying the `union_all` method
        to all geometries within a groupself.

        Observations associated with each `groupby` group will be aggregated
        using the `aggfunc`.

        Parameters
        ----------
        by : str or list-like, default None
            Column(s) whose values define the groups to be dissolved. If None,
            the entire GeoDataFrame is considered as a single group. If a list-like
            object is provided, the values in the list are treated as categorical
            labels, and polygons will be combined based on the equality of
            these categorical labels.
        aggfunc : function or string, default "first"
            Aggregation function for manipulation of data associated
            with each group. Passed to pandas `groupby.agg` method.
            Accepted combinations are:

            - function
            - string function name
            - list of functions and/or function names, e.g. [np.sum, 'mean']
            - dict of axis labels -> functions, function names or list of such.
        as_index : boolean, default True
            If true, groupby columns become index of result.
        level : int or str or sequence of int or sequence of str, default None
            If the axis is a MultiIndex (hierarchical), group by a
            particular level or levels.
        sort : bool, default True
            Sort group keys. Get better performance by turning this off.
            Note this does not influence the order of observations within
            each group. Groupby preserves the order of rows within each group.
        observed : bool, default False
            This only applies if any of the groupers are Categoricals.
            If True: only show observed values for categorical groupers.
            If False: show all values for categorical groupers.
        dropna : bool, default True
            If True, and if group keys contain NA values, NA values
            together with row/column will be dropped. If False, NA
            values will also be treated as the key in groups.
        method : str (default ``"unary"``)
            The method to use for the union. Options are:

            * ``"unary"``: use the unary union algorithm. This option is the most robust
              but can be slow for large numbers of geometries (default).
            * ``"coverage"``: use the coverage union algorithm. This option is optimized
              for non-overlapping polygons and can be significantly faster than the
              unary union algorithm. However, it can produce invalid geometries if the
              polygons overlap.
            * ``"disjoint_subset:``: use the disjoint subset union algorithm. This
              option is optimized for inputs that can be divided into subsets that do
              not intersect. If there is only one such subset, performance can be
              expected to be worse than ``"unary"``.  Requires Shapely >= 2.1.


        grid_size : float, default None
            When grid size is specified, a fixed-precision space is used to perform the
            union operations. This can be useful when unioning geometries that are not
            perfectly snapped or to avoid geometries not being unioned because of
            `robustness issues <https://libgeos.org/usage/faq/#why-doesnt-a-computed-point-lie-exactly-on-a-line>`_.
            The inputs are first snapped to a grid of the given size. When a line
            segment of a geometry is within tolerance off a vertex of another geometry,
            this vertex will be inserted in the line segment. Finally, the result
            vertices are computed on the same grid. Is only supported for ``method``
            ``"unary"``. If None, the highest precision of the inputs will be used.
            Defaults to None.

            .. versionadded:: 1.1.0
        **kwargs :
            Keyword arguments to be passed to the pandas `DataFrameGroupby.agg` method
            which is used by `dissolve`. In particular, `numeric_only` may be
            supplied, which will be required in pandas 2.0 for certain aggfuncs.

            .. versionadded:: 0.13.0

        Returns
        -------
        GeoDataFrame

        Examples
        --------
        >>> from shapely.geometry import Point
        >>> d = {
        ...     "col1": ["name1", "name2", "name1"],
        ...     "geometry": [Point(1, 2), Point(2, 1), Point(0, 1)],
        ... }
        >>> gdf = geopandas.GeoDataFrame(d, crs=4326)
        >>> gdf
            col1     geometry
        0  name1  POINT (1 2)
        1  name2  POINT (2 1)
        2  name1  POINT (0 1)

        >>> dissolved = gdf.dissolve('col1')
        >>> dissolved  # doctest: +SKIP
                                geometry
        col1
        name1  MULTIPOINT ((0 1), (1 2))
        name2                POINT (2 1)

        See Also
        --------
        GeoDataFrame.explode : explode multi-part geometries into single geometries

        """
        if by is None and level is None:
            by = np.zeros(len(self), dtype="int64")  # type: ignore [assignment]

        groupby_kwargs = {
            "by": by,
            "level": level,
            "sort": sort,
            "observed": observed,
            "dropna": dropna,
        }

        # Process non-spatial component
        data = self.drop(labels=self.geometry.name, axis=1)
        aggregated_data = data.groupby(**groupby_kwargs).agg(aggfunc, **kwargs)

        aggregated_data.columns = aggregated_data.columns.to_flat_index()

        # Process spatial component
        def merge_geometries(block):
            merged_geom = block.union_all(method=method, grid_size=grid_size)
            return merged_geom

        g = self.groupby(group_keys=False, **groupby_kwargs)[self.geometry.name].agg(
            merge_geometries
        )

        # Aggregate
        aggregated_geometry = type(self)(g, geometry=self.geometry.name, crs=self.crs)
        # Recombine
        aggregated = aggregated_geometry.join(aggregated_data)

        # Reset if requested
        if not as_index:
            aggregated = aggregated.reset_index()

        return aggregated

    # overrides the pandas native explode method to break up features geometrically
    def explode(
        self,
        column: str | None = None,
        ignore_index: bool = False,
        index_parts: bool = False,
        **kwargs,
    ) -> GeoDataFrame | DataFrame:
        """
        Explode multi-part geometries into multiple single geometries.

        Each row containing a multi-part geometry will be split into
        multiple rows with single geometries, thereby increasing the vertical
        size of the GeoDataFrame.

        Parameters
        ----------
        column : string, default None
            Column to explode. In the case of a geometry column, multi-part
            geometries are converted to single-part.
            If None, the active geometry column is used.
        ignore_index : bool, default False
            If True, the resulting index will be labelled 0, 1, …, n - 1,
            ignoring `index_parts`.
        index_parts : boolean, default False
            If True, the resulting index will be a multi-index (original
            index with an additional level indicating the multiple
            geometries: a new zero-based index for each single part geometry
            per multi-part geometry).

        Returns
        -------
        GeoDataFrame
            Exploded geodataframe with each single geometry
            as a separate entry in the geodataframe.

        Examples
        --------
        >>> from shapely.geometry import MultiPoint
        >>> d = {
        ...     "col1": ["name1", "name2"],
        ...     "geometry": [
        ...         MultiPoint([(1, 2), (3, 4)]),
        ...         MultiPoint([(2, 1), (0, 0)]),
        ...     ],
        ... }
        >>> gdf = geopandas.GeoDataFrame(d, crs=4326)
        >>> gdf
            col1               geometry
        0  name1  MULTIPOINT ((1 2), (3 4))
        1  name2  MULTIPOINT ((2 1), (0 0))

        >>> exploded = gdf.explode(index_parts=True)
        >>> exploded
              col1     geometry
        0 0  name1  POINT (1 2)
          1  name1  POINT (3 4)
        1 0  name2  POINT (2 1)
          1  name2  POINT (0 0)

        >>> exploded = gdf.explode(index_parts=False)
        >>> exploded
            col1     geometry
        0  name1  POINT (1 2)
        0  name1  POINT (3 4)
        1  name2  POINT (2 1)
        1  name2  POINT (0 0)

        >>> exploded = gdf.explode(ignore_index=True)
        >>> exploded
            col1     geometry
        0  name1  POINT (1 2)
        1  name1  POINT (3 4)
        2  name2  POINT (2 1)
        3  name2  POINT (0 0)

        See Also
        --------
        GeoDataFrame.dissolve : dissolve geometries into a single observation.

        """
        # If no column is specified then default to the active geometry column
        if column is None:
            column = self.geometry.name
        # If the specified column is not a geometry dtype use pandas explode
        if not isinstance(self[column].dtype, GeometryDtype):
            return super().explode(column, ignore_index=ignore_index, **kwargs)

        exploded_geom = self.geometry.reset_index(drop=True).explode(index_parts=True)

        df = self.drop(self._geometry_column_name, axis=1).take(
            exploded_geom.index.droplevel(-1)
        )
        df[exploded_geom.name] = exploded_geom.values
        df = df.set_geometry(self._geometry_column_name).__finalize__(self)

        if ignore_index:
            df.reset_index(inplace=True, drop=True)
        elif index_parts:
            # reset to MultiIndex, otherwise df index is only first level of
            # exploded GeoSeries index.
            df = df.set_index(
                exploded_geom.index.droplevel(
                    list(range(exploded_geom.index.nlevels - 1))
                ),
                append=True,
            )

        return df

    def to_postgis(
        self,
        name: str,
        con,
        schema: str | None = None,
        if_exists: Literal["fail", "replace", "append"] = "fail",
        index: bool = False,
        index_label: Iterable[str] | str | None = None,
        chunksize: int | None = None,
        dtype=None,
    ) -> None:
        """
        Upload GeoDataFrame into PostGIS database.

        This method requires SQLAlchemy and GeoAlchemy2, and a PostgreSQL
        Python driver (psycopg or psycopg2) to be installed.

        It is also possible to use :meth:`~GeoDataFrame.to_file` to write to a database.
        Especially for file geodatabases like GeoPackage or SpatiaLite this can be
        easier.

        Parameters
        ----------
        name : str
            Name of the target table.
        con : sqlalchemy.engine.Connection or sqlalchemy.engine.Engine
            Active connection to the PostGIS database.
        if_exists : {'fail', 'replace', 'append'}, default 'fail'
            How to behave if the table already exists:

            - fail: Raise a ValueError.
            - replace: Drop the table before inserting new values.
            - append: Insert new values to the existing table.
        schema : string, optional
            Specify the schema. If None, use default schema: 'public'.
        index : bool, default False
            Write DataFrame index as a column.
            Uses *index_label* as the column name in the table.
        index_label : string or sequence, default None
            Column label for index column(s).
            If None is given (default) and index is True,
            then the index names are used.
        chunksize : int, optional
            Rows will be written in batches of this size at a time.
            By default, all rows will be written at once.
        dtype : dict of column name to SQL type, default None
            Specifying the datatype for columns.
            The keys should be the column names and the values
            should be the SQLAlchemy types.

        Examples
        --------
        >>> from sqlalchemy import create_engine
        >>> engine = create_engine("postgresql://myusername:mypassword@myhost:5432\
/mydatabase")  # doctest: +SKIP
        >>> gdf.to_postgis("my_table", engine)  # doctest: +SKIP

        See Also
        --------
        GeoDataFrame.to_file : write GeoDataFrame to file
        read_postgis : read PostGIS database to GeoDataFrame

        """
        geopandas.io.sql._write_postgis(
            self, name, con, schema, if_exists, index, index_label, chunksize, dtype
        )

    plot = Accessor("plot", geopandas.plotting.GeoplotAccessor)

    @doc(_explore)
    def explore(self, *args, **kwargs) -> folium.Map:
        return _explore(self, *args, **kwargs)

    def sjoin(
        self,
        df: GeoDataFrame,
        how: Literal["left", "right", "inner"] = "inner",
        predicate: str = "intersects",
        lsuffix: str = "left",
        rsuffix: str = "right",
        **kwargs,
    ) -> GeoDataFrame:
        """Spatial join of two GeoDataFrames.

        See the User Guide page :doc:`../../user_guide/mergingdata` for details.

        Parameters
        ----------
        df : GeoDataFrame
        how : string, default 'inner'
            The type of join:

            * 'left': use keys from left_df; retain only left_df geometry column
            * 'right': use keys from right_df; retain only right_df geometry column
            * 'inner': use intersection of keys from both dfs; retain only
              left_df geometry column

        predicate : string, default 'intersects'
            Binary predicate. Valid values are determined by the spatial index used.
            You can check the valid values in left_df or right_df as
            ``left_df.sindex.valid_query_predicates`` or
            ``right_df.sindex.valid_query_predicates``

            Available predicates include:

            * ``'intersects'``: True if geometries intersect (boundaries and interiors)
            * ``'within'``: True if left geometry is completely within right geometry
            * ``'contains'``: True if left geometry completely contains right geometry
            * ``'contains_properly'``: True if left geometry contains right geometry
              and their boundaries do not touch
            * ``'overlaps'``: True if geometries overlap but neither contains the other
            * ``'crosses':`` True if geometries cross (interiors intersect but neither
              contains the other, with intersection dimension less than max dimension)
            * ``'touches'``: True if geometries touch at boundaries but interiors don't
            * ``'covers'``: True if left geometry covers right geometry (every point of
              right is a point of left)
            * ``'covered_by'``: True if left geometry is covered by right geometry
            * ``'dwithin'``: True if geometries are within specified distance (requires
              distance parameter)

        lsuffix : string, default 'left'
            Suffix to apply to overlapping column names (left GeoDataFrame).
        rsuffix : string, default 'right'
            Suffix to apply to overlapping column names (right GeoDataFrame).
        distance : number or array_like, optional
            Distance(s) around each input geometry within which to query the tree
            for the 'dwithin' predicate. If array_like, must be
            one-dimesional with length equal to length of left GeoDataFrame.
            Required if ``predicate='dwithin'``.
        on_attribute : string, list or tuple
            Column name(s) to join on as an additional join restriction on top
            of the spatial predicate. These must be found in both DataFrames.
            If set, observations are joined only if the predicate applies
            and values in specified columns match.

        Examples
        --------
        >>> import geodatasets
        >>> chicago = geopandas.read_file(
        ...     geodatasets.get_path("geoda.chicago_commpop")
        ... )
        >>> groceries = geopandas.read_file(
        ...     geodatasets.get_path("geoda.groceries")
        ... ).to_crs(chicago.crs)

        >>> chicago.head()  # doctest: +SKIP
                 community  ...                                           geometry
        0          DOUGLAS  ...  MULTIPOLYGON (((-87.60914 41.84469, -87.60915 ...
        1          OAKLAND  ...  MULTIPOLYGON (((-87.59215 41.81693, -87.59231 ...
        2      FULLER PARK  ...  MULTIPOLYGON (((-87.62880 41.80189, -87.62879 ...
        3  GRAND BOULEVARD  ...  MULTIPOLYGON (((-87.60671 41.81681, -87.60670 ...
        4          KENWOOD  ...  MULTIPOLYGON (((-87.59215 41.81693, -87.59215 ...

        [5 rows x 9 columns]

        >>> groceries.head()  # doctest: +SKIP
           OBJECTID     Ycoord  ...  Category                           geometry
        0        16  41.973266  ...       NaN  MULTIPOINT ((-87.65661 41.97321))
        1        18  41.696367  ...       NaN  MULTIPOINT ((-87.68136 41.69713))
        2        22  41.868634  ...       NaN  MULTIPOINT ((-87.63918 41.86847))
        3        23  41.877590  ...       new  MULTIPOINT ((-87.65495 41.87783))
        4        27  41.737696  ...       NaN  MULTIPOINT ((-87.62715 41.73623))
        [5 rows x 8 columns]

        >>> groceries_w_communities = groceries.sjoin(chicago)
        >>> groceries_w_communities[["OBJECTID", "community", "geometry"]].head()
           OBJECTID       community                           geometry
        0        16          UPTOWN  MULTIPOINT ((-87.65661 41.97321))
        1        18     MORGAN PARK  MULTIPOINT ((-87.68136 41.69713))
        2        22  NEAR WEST SIDE  MULTIPOINT ((-87.63918 41.86847))
        3        23  NEAR WEST SIDE  MULTIPOINT ((-87.65495 41.87783))
        4        27         CHATHAM  MULTIPOINT ((-87.62715 41.73623))

        Notes
        -----
        Every operation in GeoPandas is planar, i.e. the potential third
        dimension is not taken into account.

        See Also
        --------
        GeoDataFrame.sjoin_nearest : nearest neighbor join
        sjoin : equivalent top-level function
        """
        return geopandas.sjoin(
            left_df=self,
            right_df=df,
            how=how,
            predicate=predicate,
            lsuffix=lsuffix,
            rsuffix=rsuffix,
            **kwargs,
        )

    def sjoin_nearest(
        self,
        right: GeoDataFrame,
        how: Literal["left", "right", "inner"] = "inner",
        max_distance: float | None = None,
        lsuffix: str = "left",
        rsuffix: str = "right",
        distance_col: str | None = None,
        exclusive: bool = False,
    ) -> GeoDataFrame:
        """
        Spatial join of two GeoDataFrames based on the distance between their
        geometries.

        Results will include multiple output records for a single input record
        where there are multiple equidistant nearest or intersected neighbors.

        See the User Guide page
        https://geopandas.readthedocs.io/en/latest/docs/user_guide/mergingdata.html
        for more details.


        Parameters
        ----------
        right : GeoDataFrame
        how : string, default 'inner'
            The type of join:

            * 'left': use keys from left_df; retain only left_df geometry column
            * 'right': use keys from right_df; retain only right_df geometry column
            * 'inner': use intersection of keys from both dfs; retain only
              left_df geometry column

        max_distance : float, default None
            Maximum distance within which to query for nearest geometry.
            Must be greater than 0.
            The max_distance used to search for nearest items in the tree may have a
            significant impact on performance by reducing the number of input
            geometries that are evaluated for nearest items in the tree.
        lsuffix : string, default 'left'
            Suffix to apply to overlapping column names (left GeoDataFrame).
        rsuffix : string, default 'right'
            Suffix to apply to overlapping column names (right GeoDataFrame).
        distance_col : string, default None
            If set, save the distances computed between matching geometries under a
            column of this name in the joined GeoDataFrame.
        exclusive : bool, optional, default False
            If True, the nearest geometries that are equal to the input geometry
            will not be returned, default False.

        Examples
        --------
        >>> import geodatasets
        >>> groceries = geopandas.read_file(
        ...     geodatasets.get_path("geoda.groceries")
        ... )
        >>> chicago = geopandas.read_file(
        ...     geodatasets.get_path("geoda.chicago_health")
        ... ).to_crs(groceries.crs)

        >>> chicago.head()  # doctest: +SKIP
           ComAreaID  ...                                           geometry
        0         35  ...  POLYGON ((-87.60914 41.84469, -87.60915 41.844...
        1         36  ...  POLYGON ((-87.59215 41.81693, -87.59231 41.816...
        2         37  ...  POLYGON ((-87.62880 41.80189, -87.62879 41.801...
        3         38  ...  POLYGON ((-87.60671 41.81681, -87.60670 41.816...
        4         39  ...  POLYGON ((-87.59215 41.81693, -87.59215 41.816...
        [5 rows x 87 columns]

        >>> groceries.head()  # doctest: +SKIP
           OBJECTID     Ycoord  ...  Category                           geometry
        0        16  41.973266  ...       NaN  MULTIPOINT ((-87.65661 41.97321))
        1        18  41.696367  ...       NaN  MULTIPOINT ((-87.68136 41.69713))
        2        22  41.868634  ...       NaN  MULTIPOINT ((-87.63918 41.86847))
        3        23  41.877590  ...       new  MULTIPOINT ((-87.65495 41.87783))
        4        27  41.737696  ...       NaN  MULTIPOINT ((-87.62715 41.73623))
        [5 rows x 8 columns]

        >>> groceries_w_communities = groceries.sjoin_nearest(chicago)
        >>> groceries_w_communities[["Chain", "community", "geometry"]].head(2)
                       Chain    community                                geometry
        0     VIET HOA PLAZA       UPTOWN   MULTIPOINT ((1168268.672 1933554.35))
        1  COUNTY FAIR FOODS  MORGAN PARK  MULTIPOINT ((1162302.618 1832900.224))


        To include the distances:

        >>> groceries_w_communities = groceries.sjoin_nearest(chicago, \
distance_col="distances")
        >>> groceries_w_communities[["Chain", "community", \
"distances"]].head(2)
                       Chain    community  distances
        0     VIET HOA PLAZA       UPTOWN        0.0
        1  COUNTY FAIR FOODS  MORGAN PARK        0.0

        In the following example, we get multiple groceries for Uptown because all
        results are equidistant (in this case zero because they intersect).
        In fact, we get 4 results in total:

        >>> chicago_w_groceries = groceries.sjoin_nearest(chicago, \
distance_col="distances", how="right")
        >>> uptown_results = \
chicago_w_groceries[chicago_w_groceries["community"] == "UPTOWN"]
        >>> uptown_results[["Chain", "community"]]
                    Chain community
        30  VIET HOA PLAZA    UPTOWN
        30      JEWEL OSCO    UPTOWN
        30          TARGET    UPTOWN
        30       Mariano's    UPTOWN

        See Also
        --------
        GeoDataFrame.sjoin : binary predicate joins
        sjoin_nearest : equivalent top-level function

        Notes
        -----
        Since this join relies on distances, results will be inaccurate
        if your geometries are in a geographic CRS.

        Every operation in GeoPandas is planar, i.e. the potential third
        dimension is not taken into account.
        """
        return geopandas.sjoin_nearest(
            self,
            right,
            how=how,
            max_distance=max_distance,
            lsuffix=lsuffix,
            rsuffix=rsuffix,
            distance_col=distance_col,
            exclusive=exclusive,
        )

    def clip(
        self, mask, keep_geom_type: bool = False, sort: bool = False
    ) -> GeoDataFrame:
        """Clip points, lines, or polygon geometries to the mask extent.

        Both layers must be in the same Coordinate Reference System (CRS).
        The GeoDataFrame will be clipped to the full extent of the ``mask`` object.

        If there are multiple polygons in mask, data from the GeoDataFrame will be
        clipped to the total boundary of all polygons in mask.

        Parameters
        ----------
        mask : GeoDataFrame, GeoSeries, (Multi)Polygon, list-like
            Polygon vector layer used to clip the GeoDataFrame.
            The mask's geometry is dissolved into one geometric feature
            and intersected with GeoDataFrame.
            If the mask is list-like with four elements ``(minx, miny, maxx, maxy)``,
            ``clip`` will use a faster rectangle clipping
            (:meth:`~GeoSeries.clip_by_rect`), possibly leading to slightly different
            results.
        keep_geom_type : boolean, default False
            If True, return only geometries of original type in case of intersection
            resulting in multiple geometry types or GeometryCollections.
            If False, return all resulting geometries (potentially mixed types).
        sort : boolean, default False
            If True, the order of rows in the clipped GeoDataFrame will be preserved at
            small performance cost. If False the order of rows in the clipped
            GeoDataFrame will be random.

        Returns
        -------
        GeoDataFrame
            Vector data (points, lines, polygons) from the GeoDataFrame clipped to
            polygon boundary from mask.

        See Also
        --------
        clip : equivalent top-level function

        Examples
        --------
        Clip points (grocery stores) with polygons (the Near West Side community):

        >>> import geodatasets
        >>> chicago = geopandas.read_file(
        ...     geodatasets.get_path("geoda.chicago_health")
        ... )
        >>> near_west_side = chicago[chicago["community"] == "NEAR WEST SIDE"]
        >>> groceries = geopandas.read_file(
        ...     geodatasets.get_path("geoda.groceries")
        ... ).to_crs(chicago.crs)
        >>> groceries.shape
        (148, 8)

        >>> nws_groceries = groceries.clip(near_west_side)
        >>> nws_groceries.shape
        (7, 8)
        """
        return geopandas.clip(self, mask=mask, keep_geom_type=keep_geom_type, sort=sort)

    def overlay(
        self,
        right: GeoDataFrame,
        how: Literal[
            "intersection", "union", "identity", "symmetric_difference", "difference"
        ] = "intersection",
        keep_geom_type: bool | None = None,
        make_valid: bool = True,
    ):
        """Perform spatial overlay between GeoDataFrames.

        Currently only supports data GeoDataFrames with uniform geometry types,
        i.e. containing only (Multi)Polygons, or only (Multi)Points, or a
        combination of (Multi)LineString and LinearRing shapes.
        Implements several methods that are all effectively subsets of the union.

        See the User Guide page :doc:`../../user_guide/set_operations` for details.

        Parameters
        ----------
        right : GeoDataFrame
        how : string
            Method of spatial overlay: 'intersection', 'union',
            'identity', 'symmetric_difference' or 'difference'.
        keep_geom_type : bool
            If True, return only geometries of the same geometry type the GeoDataFrame
            has, if False, return all resulting geometries. Default is None,
            which will set keep_geom_type to True but warn upon dropping
            geometries.
        make_valid : bool, default True
            If True, any invalid input geometries are corrected with a call to
            make_valid(), if False, a `ValueError` is raised if any input geometries
            are invalid.

        Returns
        -------
        df : GeoDataFrame
            GeoDataFrame with new set of polygons and attributes
            resulting from the overlay

        Examples
        --------
        >>> from shapely.geometry import Polygon
        >>> polys1 = geopandas.GeoSeries([Polygon([(0,0), (2,0), (2,2), (0,2)]),
        ...                               Polygon([(2,2), (4,2), (4,4), (2,4)])])
        >>> polys2 = geopandas.GeoSeries([Polygon([(1,1), (3,1), (3,3), (1,3)]),
        ...                               Polygon([(3,3), (5,3), (5,5), (3,5)])])
        >>> df1 = geopandas.GeoDataFrame({'geometry': polys1, 'df1_data':[1,2]})
        >>> df2 = geopandas.GeoDataFrame({'geometry': polys2, 'df2_data':[1,2]})

        >>> df1.overlay(df2, how='union')
           df1_data  df2_data                                           geometry
        0       1.0       1.0                POLYGON ((2 2, 2 1, 1 1, 1 2, 2 2))
        1       2.0       1.0                POLYGON ((2 2, 2 3, 3 3, 3 2, 2 2))
        2       2.0       2.0                POLYGON ((4 4, 4 3, 3 3, 3 4, 4 4))
        3       1.0       NaN      POLYGON ((2 0, 0 0, 0 2, 1 2, 1 1, 2 1, 2 0))
        4       2.0       NaN  MULTIPOLYGON (((3 4, 3 3, 2 3, 2 4, 3 4)), ((4...
        5       NaN       1.0  MULTIPOLYGON (((2 3, 2 2, 1 2, 1 3, 2 3)), ((3...
        6       NaN       2.0      POLYGON ((3 5, 5 5, 5 3, 4 3, 4 4, 3 4, 3 5))

        >>> df1.overlay(df2, how='intersection')
           df1_data  df2_data                             geometry
        0         1         1  POLYGON ((2 2, 2 1, 1 1, 1 2, 2 2))
        1         2         1  POLYGON ((2 2, 2 3, 3 3, 3 2, 2 2))
        2         2         2  POLYGON ((4 4, 4 3, 3 3, 3 4, 4 4))

        >>> df1.overlay(df2, how='symmetric_difference')
           df1_data  df2_data                                           geometry
        0       1.0       NaN      POLYGON ((2 0, 0 0, 0 2, 1 2, 1 1, 2 1, 2 0))
        1       2.0       NaN  MULTIPOLYGON (((3 4, 3 3, 2 3, 2 4, 3 4)), ((4...
        2       NaN       1.0  MULTIPOLYGON (((2 3, 2 2, 1 2, 1 3, 2 3)), ((3...
        3       NaN       2.0      POLYGON ((3 5, 5 5, 5 3, 4 3, 4 4, 3 4, 3 5))

        >>> df1.overlay(df2, how='difference')
                                                    geometry  df1_data
        0      POLYGON ((2 0, 0 0, 0 2, 1 2, 1 1, 2 1, 2 0))         1
        1  MULTIPOLYGON (((3 4, 3 3, 2 3, 2 4, 3 4)), ((4...         2

        >>> df1.overlay(df2, how='identity')
           df1_data  df2_data                                           geometry
        0         1       1.0                POLYGON ((2 2, 2 1, 1 1, 1 2, 2 2))
        1         2       1.0                POLYGON ((2 2, 2 3, 3 3, 3 2, 2 2))
        2         2       2.0                POLYGON ((4 4, 4 3, 3 3, 3 4, 4 4))
        3         1       NaN      POLYGON ((2 0, 0 0, 0 2, 1 2, 1 1, 2 1, 2 0))
        4         2       NaN  MULTIPOLYGON (((3 4, 3 3, 2 3, 2 4, 3 4)), ((4...

        See Also
        --------
        GeoDataFrame.sjoin : spatial join
        overlay : equivalent top-level function

        Notes
        -----
        Every operation in GeoPandas is planar, i.e. the potential third
        dimension is not taken into account.
        """
        return geopandas.overlay(
            self, right, how=how, keep_geom_type=keep_geom_type, make_valid=make_valid
        )


def _dataframe_set_geometry(
    self,
    col,
    drop: bool | None = None,
    inplace: Literal[False] = False,
    crs: Any | None = None,
) -> GeoDataFrame:
    if inplace:
        raise ValueError(
            "Can't do inplace setting when converting from DataFrame to GeoDataFrame"
        )
    gf = GeoDataFrame(self)
    # this will copy so that BlockManager gets copied
    return gf.set_geometry(col, drop=drop, inplace=False, crs=crs)


DataFrame.set_geometry = _dataframe_set_geometry
